1Z0-1127-25 OCI Generative AI Service Practice Question
A team fine-tuned a Cohere Command R model using the T-Few technique on a dataset of JSONL prompt/completion pairs. After deployment, they observe that the model's responses are too repetitive. Which parameter adjustment in the OCI Generative AI Playground would BEST address this issue?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase frequency penalty
Increasing the frequency penalty discourages the model from repeating tokens that have already appeared, reducing repetitive outputs. Temperature and max tokens address randomness and length, not repetition specifically. Stop sequences are for terminating generation early.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase frequency penalty
Why this is correct
Increasing the frequency penalty reduces the likelihood of the model repeating the same tokens or phrases by subtracting a penalty value from the logits of tokens that have already appeared in the generated text. This directly addresses the repetitive responses observed after fine-tuning with the T-Few technique, which can overfit to common patterns in the JSONL prompt/completion pairs.
- ✗
Increase max tokens
Why it's wrong here
Max tokens controls output length, not repetition.
- ✗
Decrease temperature
Why it's wrong here
Lower temperature makes outputs more deterministic, which can exacerbate repetition.
- ✗
Add a stop sequence
Why it's wrong here
Stop sequences terminate generation but do not reduce repetition within the generated text.
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